%% US Public Debt and Safe Asset Market Power
%% Jason Choi, Rishabh Kirpalani, and Diego Perez
%% Nov 24, 2024

%% Solve Monopoly Equilibrium w/ Alternate Benefit Function

%----------------------------------------------------------------
% 0. Housekeeping
%----------------------------------------------------------------

close all

%----------------------------------------------------------------
% 1. Defining variables
%----------------------------------------------------------------

// Endogenous Variables
var b rb rkstar rk krw_star kus_star krw kus kstar k wstar w c_rw c_us drdb spread vrw vus dMrwdb y;

// Exogenous Variables
var nnu oomega A Astar;

// Shocks
varexo eps_nnu eps_oomega eps_A;

// Parameters
parameters ggamma bbeta eeta llambda aalpha Astarbar Abar iiota iiota_star ddelta_rw ddelta_us
  nnu_bar oomega_bar rrho_nnu rrho_oomega ssigma_nnu ssigma_oomega rrho_A ssigma_A rrho_Astar ssigma_Astar
  b_me rb_me rkstar_me rk_me krw_star_me kus_star_me krw_me kus_me kstar_me k_me
  capKstar_me capK_me wstar_me w_me crw_me cus_me spread_me vrw_me vus_me;

%----------------------------------------------------------------
% 2. Calibration
%----------------------------------------------------------------

% // Parameters
ggamma = 2;
bbeta = 0.9886;
eeta = 0.997;
llambda = 1;
aalpha = 0.3;
Astarbar = 0.9254;
Abar = 0.8154;
iiota = 0.9070;
iiota_star = 0.7939;
ddelta_rw = 0.1;
ddelta_us = 0.1;
nnu_bar = 2.1751;
oomega_bar = 0.0056;
rrho_nnu = 0.99;
ssigma_nnu = 0.01;
rrho_oomega = 0.95;
ssigma_oomega = 0.3;
rrho_A = 0.95;
ssigma_A = 0.02;
rrho_Astar = rrho_A;
ssigma_Astar = ssigma_A;

% Analytic Steady State
[bss] = fsolve(@(b) func_ireland(eeta,nnu_bar,llambda,oomega_bar,'me',b),0.41);
b_me = bss;
rb_me = 1/bbeta - ((eeta-1)*log(b_me)+(eeta-1)-nnu_bar*(eeta-1)) - 1;
rkstar_me = 1/bbeta + ddelta_rw - 1;
rk_me = 1/bbeta + ddelta_us - 1;
krw_star_me = ((aalpha*(1-iiota_star)*Astarbar*((1/bbeta+ddelta_rw-1)/(aalpha*iiota_star*Astarbar))^((aalpha*(1-iiota_star)-1)/(aalpha*(1-iiota_star))))/(1/bbeta+ddelta_us-1))^((aalpha*(1-iiota_star))/(1-aalpha));
kus_star_me = ((1/bbeta+ddelta_rw-1)/(aalpha*iiota_star*Astarbar))^(1/(aalpha*(1-iiota_star)))*krw_star_me^((1-iiota_star*aalpha)/(aalpha*(1-iiota_star)));
krw_me = ((aalpha*iiota*Abar*((1/bbeta+ddelta_us-1)/(aalpha*(1-iiota)*Abar))^((aalpha*iiota-1)/(aalpha*iiota)))/(1/bbeta+ddelta_rw-1))^((aalpha*iiota)/(1-aalpha));
kus_me = ((1/bbeta+ddelta_us-1)/(aalpha*(1-iiota)*Abar))^(1/(aalpha*iiota))*krw_me^((1-(1-iiota)*aalpha)/(aalpha*iiota));
kstar_me = krw_star_me + krw_me;
k_me = kus_star_me + kus_me;
capKstar_me = krw_star_me^iiota_star*kus_star_me^(1-iiota_star);
capK_me = krw_me^(1-iiota)*kus_me^iiota;
wstar_me = Astarbar*(1-aalpha)*(capKstar_me)^aalpha;
w_me = Abar*(1-aalpha)*(capK_me)^aalpha;
crw_me = wstar_me + (rkstar_me-ddelta_rw)*kstar_me + (eeta-1)*b_me*(log(b_me)-nnu_bar) + rb_me*b_me;
cus_me = w_me + (rk_me-ddelta_us)*k_me - oomega_bar/(1+llambda)*(b_me)^(1+llambda) - rb_me*(b_me);
drdb_me = -(eeta-1)/b_me;
dMrwdb_me = 0;
nnu_me = nnu_bar;
oomega_me = oomega_bar;
A_me = Abar;
Astar_me = Astarbar;
spread_me = (rk_me-ddelta_us-rb_me);
vrw_me = crw_me^(1-ggamma)/(1-ggamma)/(1-bbeta);
vus_me = cus_me^(1-ggamma)/(1-ggamma)/(1-bbeta);
y_me = A_me*(kus_me^iiota*krw_me^(1-iiota))^aalpha;

%----------------------------------------------------------------
% 3. Model
%----------------------------------------------------------------

model;

c_rw^(-ggamma) = bbeta*c_rw(+1)^(-ggamma)*((eeta-1)*log(b)+(eeta-1)-nnu*(eeta-1)+1+rb);
c_rw^(-ggamma) = bbeta*c_rw(+1)^(-ggamma)*(1-ddelta_rw+rkstar);
c_rw + kstar + b = wstar + (1-ddelta_rw+rkstar(-1))*kstar(-1) + (eeta-1)*b*(log(b)-nnu) + (1+rb(-1))*b(-1);

c_us^(-ggamma) = bbeta*c_us(+1)^(-ggamma)*(oomega*b^llambda+1+rb+drdb*b);
c_us^(-ggamma) = bbeta*c_us(+1)^(-ggamma)*(1-ddelta_us+rk);
c_us + k - b = w + (1-ddelta_us+rk(-1))*k(-1) - oomega(-1)/(1+llambda)*(b(-1))^(1+llambda) - (1+rb(-1))*b(-1);

rk = Astar*aalpha*(1-iiota_star)*krw_star^(aalpha*iiota_star)*kus_star^(aalpha*(1-iiota_star)-1);
rkstar = Astar*aalpha*iiota_star*krw_star^(aalpha*iiota_star-1)*kus_star^(aalpha*(1-iiota_star));
rk = A*aalpha*iiota*krw^(aalpha*(1-iiota))*kus^(aalpha*iiota-1);
rkstar = A*aalpha*(1-iiota)*krw^(aalpha*(1-iiota)-1)*kus^(aalpha*iiota);
wstar = Astar*(1-aalpha)*krw_star^(aalpha*iiota_star)*kus_star^(aalpha*(1-iiota_star));
w = A*(1-aalpha)*krw^(aalpha*(1-iiota))*kus^(aalpha*iiota);

0 = -(drdb+(eeta-1)/b)*(c_rw(+1)/c_rw)^(-ggamma)+(1+rb+(eeta-1)*log(b)+(eeta-1)-nnu*(eeta-1))*dMrwdb;
0 = dMrwdb*(rkstar+1-ddelta_rw);

k = kus + kus_star;
kstar = krw + krw_star;

log(nnu) = (1-rrho_nnu)*log(nnu_bar) + rrho_nnu*log(nnu(-1)) + ssigma_nnu*eps_nnu;
log(oomega) = (1-rrho_oomega)*log(oomega_bar) + rrho_oomega*log(oomega(-1)) + ssigma_oomega*eps_oomega;
log(A) = (1-rrho_A)*log(Abar) + rrho_A*log(A(-1)) + ssigma_A*eps_A;
log(Astar) = (1-rrho_Astar)*log(Astarbar) + rrho_Astar*log(Astar(-1)) + ssigma_Astar*eps_A;

spread = (rk-ddelta_us-rb);

vrw = c_rw^(1-ggamma)/(1-ggamma) + bbeta*vrw(+1);
vus = c_us^(1-ggamma)/(1-ggamma) + bbeta*vus(+1);

y = A*(kus^iiota*krw^(1-iiota))^aalpha;

end;

%----------------------------------------------------------------
% 4. Computation
%----------------------------------------------------------------

initval;
  b = b_me;
  rb = rb_me;
  rkstar = rkstar_me;
  rk = rk_me;
  krw_star = krw_star_me;
  kus_star = kus_star_me;
  krw = krw_me;
  kus = kus_me;
  kstar = kstar_me;
  k = k_me;
  wstar = wstar_me;
  w = w_me;
  c_rw = crw_me;
  c_us = cus_me;
  drdb = drdb_me;
  dMrwdb = dMrwdb_me;
  nnu = nnu_me;
  oomega = oomega_me;
  spread = spread_me;
  A = A_me;
  Astar = Astar_me;
  vrw = vrw_me;
  vus = vus_me;
  y = y_me;
end;

resid;
check;

shocks;
  var eps_nnu = 1;
  var eps_oomega = 1;
  var eps_A = 1;
end;

set_dynare_seed('default');
stoch_simul(order=2,noprint,nograph,periods=100000,pruning);

%----------------------------------------------------------------
% 5. Generate moments
%----------------------------------------------------------------

spread_path = (rk-ddelta_us-rb)*100;
var_sp = var(spread_path);
auto_sp = autocorr(spread_path);
cost = oomega./(1+llambda).*(b).^(1+llambda);
benefit = (eeta-1).*b.*(log(b)-nnu);
var_by = var(b./y);
auto_by = autocorr(b./y);
corr_pq_by = corr(spread_path,b./y);

moments = [mean(b./y) mean(spread_path) var_by var_sp corr_pq_by auto_by(2) auto_sp(2)]';
data_mom = [0.41 0.62 0.03 0.086 -0.56 0.96 0.70]';
rowNames = {'Mean b/y','Mean sp','Var b/y','Var sp','Corr (b/y,sp)','Autocorr b/y','Autocorr sp'};
colNames = {'Model Moments','Data Moments'};
TableA0 = array2table([moments data_mom],'RowNames',rowNames,'VariableNames',colNames)

deficit = cost(1:end-1) + b(2:end) - (1+rb(1:end-1)).*b(1:end-1);
deficit_y = deficit./y(1:end-1);
ca = -(b(2:end)-b(1:end-1)) + kus_star(2:end)-kus_star(1:end-1) - (krw(2:end)-krw(1:end-1));
ca_y = ca./y(1:end-1);
nfa = - b + kus_star - krw;
nfa_y = nfa./y;
var_ca_y = var(ca_y);
var_nfa_y = var(nfa_y);
var_deficit_y = var(deficit_y);
corr_nfa_y = corr(nfa_y,b./y);
corr_ca_def_y = corr(ca_y,deficit_y);

moments = [mean(rb) var(rb)*100 var_ca_y*100 var_nfa_y var_deficit_y corr_nfa_y corr_ca_def_y]';
data_mom = [0.0053 0.00097*100 0.00033*100 0.035 0.002 -0.654 -0.207]';
rowNames = {'Mean rb (Targeted)','Var rb(x100)','Var CA(x100)','Var NFA','Var Deficit','Corr(NFA,b)','Corr(CA,deficit)'};
colNames = {'Model Moments','Data Moments'};
TableA0 = array2table([moments data_mom],'RowNames',rowNames,'VariableNames',colNames)

%----------------------------------------------------------------
% 6. Calculate welfare from transition
%---------------------------------------------------------------

b_me_alt = mean(b);
rb_me_alt = mean(rb);
spread_me_alt = mean(spread);

oo_me_alt = oo_;
M_me_alt = M_;
options_me_alt = options_;

save me_alt_save oo_me_alt vus_me vrw_me cus_me crw_me M_me_alt options_me_alt b_me_alt spread_me_alt rb_me_alt;
